Yexi JiangResume

34849 Blackstone WayFremont, 94555, CA

H 305-458-8476B yexijiang@gmail.com

GitHub: https://github.com/yxjiang ,Homepage: http://yxjiang.github.io/ ,

LinkedIn: https://www.linkedin.com/in/yxjiang

SummaryA passionate applied researcher and software developer with hands-on skills on data mining/machinelearning algorithm development and research. Extensive R&D experience at the world’s leading companiesand open source community like Facebook, IBM, Microsoft, and Apache Software Foundation. More than20 research paper published at recognized international conferences and journals. Strong eagerness towork with talented and energetic people on promising projects.

ExperienceIndustrial Experience

05/2015 Senior Research Scientist, Facebook Inc., Menlo Park, CA, USA.{ Tech lead of Facebook’s friend recommendation machine learning system.

{ Increased Facebook’s Monthly Active People (MAP) by 3.7M through various friend recommen-dation optimization, including the integration of neural network and large scale sparse logisticregression and the improvement of training data quality.

{ Refactored the friend recommendation backend, improved friend recommendation feature log-ging efficiency by 30x and increased the training data collection volume by 25x.

Summer of2014

Intern, Facebook Inc., Menlo Park, CA, USA.{ Designed and implemented the pipeline as well as the related analytics tools for personalized

News Feed ranking at Internet scale, making the recommendation more accurate for active usersin Facebook.

Summer of2011, 2012,

2013

Intern, IBM T.J Watson Research Center, New York, NY, USA.{ Participated in the design and implementation of an interactive service retrieval system called

Cloud Services Marketplace to facilitate the cloud services acquisition. This prototype evolvedinto IBM 2013 Global Technology Outlook project “Scalable Services Ecosystem".

{ Designed and implemented a time-series prediction algorithm to help the cloud capacity planningand reduce the VM fulfillment time of IBM’s Smart Cloud Enterprise from the perspective ofresource prediction.

{ Participated in the design and implementation of a tool that leverages data mining techniques toimprove the efficiency for both the customers and service providers during server configuration.

03/2009-07/2009

Intern, Microsoft Research, Beijing, China.{ Designed and implemented a distributed frequent sequential mining algorithm to discover the

users’ popular operation sequences from the log of Office software to help the UI designersameliorate the layout of the next version of Office.

Open Source ProjectsApache

SoftwareFoundation

PMC Member, Apache Hama: a BSP computing framework on top of Hadoop.{ Working on the distributed machine learning package, including the design and implementation

of distributed multilayer perceptron, linear regression, logistic regression, and auto-encoder.[GitHub Link]

1

Academic08/2010-04/2015

Research Assistant, Knowledge Discovery Research Group, FIU, Miami, FL, USA.{ Designing and implementing a distributed monitoring system to conduct the continuous moni-

toring over the computer clusters. [GitHub Link]

{ Participating in the design and implementation of FIU-Miner: An integrated and user-friendlydata mining system under distributed environment. [Web Page Link]

Programming SkillsLanguages Java (proficient), Python (proficient),

C/C++ (familiar), Scala (prior experi-ence)

Frameworks& Libries

Apache Hive, Apache Hama, ApacheHadoop, ActiveMQ, Storm

Patents1. Cloud Provisioning Accelerator. With Rong Chang, Mihwa Choi, Meir Laker, Chang-Shing Perng,

Hidayatullah Shaikh, Edward So, Tao Tao. US 20130139152.

2. Interactive Acquisition of Remote Services. With Rahul Akolkar, Thomas Chefalas, Jim Laredo, Chang-Shing Perng, Anca Sailer, Frank Schaffa, Alla Segal, Ignacio Silva-Lepe, Tao Tao, Yang Zhou. US20140337010.

3. Complex Service Network Ranking and Clustering. With Rahul Akolkar, Thomas Chefalas, Jim Laredo,Chang-Shing Perng, Anca Sailer, Frank Schaffa, Alla Segal, Ignacio Silva-Lepe, Tao Tao, Yang Zhou.Filed.

Research{ Published 20+ papers at the top journals/conferences in the area of data science, such as: ACM

Conference on Knowledge Discovery and Data Mining (SIGKDD), International Conference on DataMining (ICDM), ACM Conference on Recommender Systems (RecSys), ACM Conference on Informa-tion Retrieval (SIGIR), ACM Conference on Information and Knowledge Management (CIKM), IEEETransactions on Knowledge and Data Engineering (TKDE), ACM Transactions on Intelligent Sys-tems and Technology (TIST), etc. (Details at: https://scholar.google.com/citations?user=ojlglBsAAAAJ&hl=en)

{ Program committee member and reviewer of the top academic conferences/journals in the area of datascience, such as: ACM Conference on Knowledge Discovery and Data Mining (SIGKDD), Internati-onal Conference on Data Mining (ICDM), ACM Conference on Recommender Systems (RecSys), IEEETransactions on Knowledge and Data Engineering (TKDE), ACM Transactions on Knowledge Disco-very from Data (TKDD), Knowledge and Information System (KAIS), Information Sciences, IEEETransactions on Cybernetics, Data and Knowledge Engineering (DKE), etc.

Education2015 Ph.D. in Computer Science, Florida International University, Miami, FL,USA.

Research Area: Temporal Data Mining, Distributed Data Mining2010 B.S and M.S. in Computer Science, Sichuan University, Chengdu, Sichuan,China.

Research Area: Data Mining

2